Intelligent Fault Diagnosis of Rolling Element Bearings Based on Modified AlexNet

Abstract

The reliable and safe operation of industrial systems needs to detect and diagnose bearing faults as early as possible. Intelligent fault diagnostic systems that use deep learning convolutional neural network (CNN) techniques have achieved a great deal of success in recent years. In a traditional CNN, the fully connected layer is located in the final three layers, and such a layer consists of multiple layers that are all connected. However, the fully connected layer of the CNN has the disadvantage of too many training parameters, which makes the model training and testing time longer and incurs overfitting. Additionally, because the working load is constantly changing and noise from the place of operation is unavoidable, the efficiency of intelligent fault diagnosis techniques suffers great reductions. In this research, we propose a novel technique that can effectively solve the problem of traditional CNN and accurately identify the bearing fault. Firstly, the best pre-trained CNN model is identified by considering the classification’s success rate for bearing fault diagnosis. Secondly, the selected CNN model is modified to effectively reduce the parameter quantities, overfitting, and calculating time of this model. Finally, the best classifier is identified to make a hybrid model concept to achieve the best performance. It is found that the proposed technique performs well under different load conditions, even in noisy environments, with variable signal-to-noise ratio (SNR) values. Our experimental results confirm that this proposed method is highly reliable and efficient in detecting and classifying bearing faults.

Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal

“…Fault category Experiment samples Diagnosis accuracy (%) [227] FKP-SGECNN 10 -99.63 [228] Improved graph convolutional network (GCN) 28 1400 99.12 [229] BCMFDE-RF-mRMR-KNN 10 550 99.09 [230] NWMF-CNN 10 4000 99.80 [231] Reinforcement neural architecture search CNN 12 3600 99.65 [232] Dimension expansion and AntisymNet lightweight CNN 10 10 000 99.70 [233] Multi-scale weighted graph-MCGCN 10 3000 99.45 [234] Improved (ICEEMDAN)-ICA-FuEn 10 10 000 99.91 [235] MAM-DSDCNN 7 2800 99.63 [236] Sparse representation deep learning (SR-DEEP) 4 2000 100.00 [237] Online sequential extreme learning machine (OS-ELM) 4 9466 99.62 [238] IFE + CBAM-enhanced InceptionNet 10 1000 99.5 [239] SSCL method based on MSA mechanism and MCL 10 2000 99.97 [240] MRDNN-AG 10 120 000 98.85 [241] AMCEEMD-1DCNN 7 3500 99.50 [242] Modified AlexNet-SVM 4 -99.60 [243] FC-CLDCNN 10 10 000 99.95 [244] PCA-ICEEMDAN and BiLSTM-SCN-CCAM 10 1024 99.92 [245] 2ADA + MK-MMD 10 1960 99.76 [246] 1D feature matching domain adaptation 3 9000 100.00 [247] ICEEMDAN-Hilbert transform-CBAM 10 30 000 95.2 [248] Ensemble MSRCNN-BiLSTM 4 4800 98.43 [249] WKN-BiLSTM-AM 10 1750 99.7 [250] MVO-MOMEDA-SVM 4 400 92.50 [251] WPDPCC-DGCL 10 6000 98.65 [252] I-PixelHop framework based on Spark-GPU 10 -98.93…”

Research on an Improved Auxiliary Classifier Wasserstein Generative Adversarial Network with Gradient Penalty Fault Diagnosis Method for Tilting Pad Bearing of Rotating Equipment

“…Zhong et al [5] proposed a rolling bearing fault diagnosis method based on a convolutional autoencoder and nearest-neighbor algorithm, which was verified experimentally by using the experimental data set published by CWRU under different working conditions. Mohiuddin et al [6] proposed an improved AlexNet-based intelligent fault diagnosis method for rolling bearings, which was verified experimentally using the data of different working conditions and a different signal-to-noise ratio of the experimental data set published by CWRU. Cui et al [7] proposed a method for fault diagnosis of rolling bearings under the condition of sample imbalance based on CNN, and used the conventional rolling bearing-fault data set collected in the laboratory for experimental verification.…”

The Fault Diagnosis of Rolling Bearings Is Conducted by Employing a Dual-Branch Convolutional Capsule Neural Network

“…The result is method 6 in Figure 13; 7. In reference [28], data were annotated with a singular label, proposing an enhanced AlexNet model for the diagnosis of rolling bearings. The optimal pre-training was determined based on the classification diagnostic rate.…”